Abnormal Situations Detection and Analysis through Fear-type Acoustic Manifestations

Recent work on emotional speech processing has demonstrated the interest to consider the information conveyed by the emotional component in speech to enhance the understanding of human behaviors. But to date, there has been little integration of emotion detection systems in effective applications. The present research focuses on the development of a fear-type emotions recognition system to detect and analyze abnormal situations for surveillance applications. The Fear vs. Neutral classification gets a mean accuracy rate at 70.3%. It corresponds to quite optimistic results given the diversity of fear manifestations illustrated in the data. More specific acoustic models are built inside the fear class by considering the context of emergence of the emotional manifestations, i.e. the stage of the threat during which they occur, and which has a strong influence on fear acoustic manifestations. The potential use of these models for a threat stage recognition system is also investigated. Such information about the situation can indeed be useful for surveillance systems.